I obtained my BS from Xi'an Jiaotong University in 2009 and PhD from Tsinghua University in 2014. I went to MIT to do the postdoctoral research work for three years. Then I joined the Department of Physics in HKUST in 2017.

I have a very broad interest in condensed matter physics, from the traditional phenomena like ferroelectricity to the exotic topological phases like quantum spin Hall insulators. Currently, my research focuses on two parts: 1) the novel topological phases of matter, especially on symmetry-related topological phases, and their material realizations and experimental signatures; 2) applications of advanced machine learning techniques in physics, especially the combination of machine learning techniques and quantum Monte Carlo simulations.

In my previous works, by combining various theoretical calculations and analysis with my collaborators, we predicted monolayer WTe2 type of two-dimensional transition metal dichalcogenides to be quantum spin Hall insulator and bulk PbxSn1-x(Se,Te) to be three-dimensional (3D) TCIs. Both predictions have been confirmed by several independent experimental groups. Also, collaborating with experimental group, we discovered the thinnest ferroelectric material in SnTe thin films, and proposed a new type of non-volatile random-access memory device and have filed a patent for the device. In order to further study those novel topological phenomena in strongly correlated systems, we recently developed a new numerical method, dubbed self-learning Monte Carlo (SLMC), by combining machine learning techniques and Monte Carlo simulations. SLMC can be more that 1000 faster than traditional MC methods, and is very powerful to study strongly-correlated systems of large size.